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Bereits im Bachelorstudium gehören in den Biowissenschaften Laborpraktika zum Alltag der Studierenden. Üblicherweise arbeiten die Studierenden ein vorgegebenes Skript ab und protokollieren ihre Ergebnisse, um sich grundsätzliche Techniken anzueignen. Während diese Struktur für die Anforderungen des Grundstudiums sehr gut geeignet ist, erschwert das Festhalten daran in Masterstudiengängen eine Weiterentwicklung der Studierenden zu unabhängigen Forscher*innen. Deshalb wurde hier ein Laborpraktikum für Masterstudierende anhand der Konzepte zum forschenden Lernen (FoL) geplant und durchgeführt. Studierende konnten so durch das selbständige Durchlaufen des kompletten biowissenschaftlichen Forschungszyklus Fähigkeiten erlangen, die im weiteren Verlauf ihrer Karriere für erfolgreiche eigenständige Forschung notwendig sind. Konkret wurde zu Beginn des Praktikums eine wissenschaftliche Beobachtung vorgestellt und in einer gemeinsamen Diskussion mögliche Fragestellungen, Hypothesen und experimentelle Ansätze erarbeitet. Die Studierenden haben unter Anleitung Versuche geplant, durchgeführt und ausgewertet. Die Forschungsergebnisse wurden in einem schriftlichen Protokoll im Stil einer wissenschaftlichen Publikation zusammengefasst. Die Teilnehmer*innen legten ein hohes Maß an Kreativität und Eigeninitiative an den Tag und ihr Feedback zur Veranstaltung war sehr positiv. Zusammenfassend bietet die Orientierung an den Prinzipien des forschenden Lernens für Laborpraktika auf Masterniveau bei gleichem Zeitaufwand für die Lehrenden deutlich mehr Lernpotential für die Studierenden. Daher sollten Lehrende anstreben, ihre Veranstaltungen bewusst an diesen Prinzipien auszurichten., HINT. Heidelberg Inspirations for Innovative Teaching, Bd. 3 (2022): HINT. Heidelberg Inspirations for Innovative Teaching

Additional file 1 : Supplementary Fig. 1. Tumor metastasis and growth in vivo. Supplementary Fig. 2. Cell death in TNBC cells upon isomiR overexpression. Supplementary Fig. 3. Gene set enrichment analysis of mass spectrometry data. Supplementary Fig. 4. Schematic representation of the predicted target site for miR-183-5p|+2 in the 3’UTR of E2F1. Supplementary Fig. 5. Knockdown efficiency of siE2F1 on the protein level. Supplementary Fig. 6. Validation of E2F1 overexpressing TNBC cell lines by Western Blot. Supplementary Fig. 7. Correlation plots of E2F1 and MYC expression and GSEA plots for the correlation of E2F targets and MYC targets gene sets with miR-183-5p in TCGA and METABRIC datasets. Supplementary Fig. 8. Bubble Heatmap of GSEA results of gene sets associated with miR-183-5p expression in different PAM50 subtypes in TCGA and METABRIC. Supplementary Fig. 9. Correlation of E2F and MYC activity scores with miR-183-5p in patient datasets.

This dataset consists of processed data from CHIP-Seq experiments (peaks as bed files, and signals as bigWig files), as well as whole genome bisulfite experiments (methlyation calls as bedGraph files). Here, we profiled histone modifications (H3K27ac, H3K27me3, and H3K4me3), as well as two transcription factors (CTCF, and NR4A3), from 3 acinar cell carcinoma, as well as one normal parotid gland sample. Furthermore, we profiled DNA methylation for three acinar cell carcinoma, as well as three normal parotid gland samples. In addition you find RNA-Seq count-, and fpkm tables for 13 human samples (10 tumor samples, 3 normal samples), as well as RNA-Seq count-, and fpkm tables for 6 mouse samples (3 NR4A3 ORF transfected mouse cell lines, as well as 3 control mouse cell lines with transfected redFF construct). The following files make up the dataset: ChIP-Seq peak files (bed format) AciCC1_CTCF_peaks_2167aeb49f637ea6a124e3679ce1722d.bed AciCC1_H3K27Ac_peaks_8e63eed24d0cc6cb362aa8c8da6f74ed.bed AciCC1_H3K27me3_peaks_7ba6a30bdabbf46f6e67deab447f5f98.bed AciCC1_H3K4me3_peaks_88cdce01aeededcdae869c5a68657d89.bed AciCC1_NR4A3_peaks_02be26bf1e905bcbdbb222cba4c13133.bed AciCC1_super_enhancer_peaks_3c3622d06160797a049f1e5b35efb068.bed AciCC2_CTCF_peaks_3b0fde69ad4c01f71d4a5ef07f1a8daf.bed AciCC2_H3K27Ac_peaks_36d1d3116fa41220ea57494961972245.bed AciCC2_H3K27me3_peaks_7f8f1745a20638c9db7f24c101556d12.bed AciCC2_H3K4me3_peaks_838e68c7c58e69ff02c6a9cea9bdfda9.bed AciCC2_NR4A3_peaks_9a5244e979b51e870b16fd2f072370ec.bed AciCC2_super_enhancer_peaks_5714ac0f2fd6ef3cf59d69d5114356e4.bed AciCC3_CTCF_peaks_c611c1d548f65c802f4f88aedba9f768.bed AciCC3_H3K27Ac_peaks_97e804349bba944e7d5d1b8ede631466.bed AciCC3_H3K27me3_peaks_fb2e726ae020cdbe2aa9512e31ea0d41.bed AciCC3_H3K4me3_peaks_f783774d435cce4e7b412c68ec40939d.bed AciCC3_NR4A3_peaks_af3ec0dea8c10b071a58b348f9148080.bed AciCC3_super_enhancer_peaks_ff95672c1b911065d69046579a65992d.bed Parotid_Gland3_CTCF_peaks_651723dd871e8a81ca6890e05137880b.bed Parotid_Gland3_H3K27Ac_peaks_126438a74560e776bcf8ec776cdd4640.bed Parotid_Gland3_H3K27me3_peaks_a3930e0c6493f6297169e80148ae8fef.bed Parotid_Gland3_H3K4me3_peaks_186fc855845537c54dc14bd16821f812.bed Parotid_Gland3_NR4A3_peaks_05be8421f1989fd1a9b0a0bbf7cd6a6f.bed Parotid_Gland3_super_enhancer_peaks_35c87c26ee2f9666ae6e24b3dd376e68.bed ChIP-Seq signal files (bigWig format) AciCC1_CTCF_chip_signal_7d1b78e4c5ce944e24440dbdfd5b908b.bw AciCC1_H3K27Ac_chip_signal_f2cfdda403ecfcf67c0b6045f490137b.bw AciCC1_H3K27me3_chip_signal_dfa1a91a9bef6c9b456615e62aa2f951.bw AciCC1_H3K4me3_chip_signal_17c9ebe6f38bd5c7d54487752e29c3f5.bw AciCC1_NR4A3_chip_signal_c2e112bf5926f70e21d0b278e5f45635.bw AciCC2_CTCF_chip_signal_0f3bcab38c4608acd7bd65e39898dae7.bw AciCC2_H3K27Ac_chip_signal_f6e037d33b45d599748edd5c8b129e91.bw AciCC2_H3K27me3_chip_signal_b4393172fc7b35d55075d61d42b1a429.bw AciCC2_H3K4me3_chip_signal_fd7ac24b019113f4947acaf4bd53f12f.bw AciCC2_NR4A3_chip_signal_34d46d654f7c7c10856e1a200a9177a4.bw AciCC3_CTCF_chip_signal_fb5252020f1773654d5e56a849f2efe3.bw AciCC3_H3K27Ac_chip_signal_2ee96af07c5d1e02147b174e6001e462.bw AciCC3_H3K27me3_chip_signal_ce6b1014eacea2638df00b35bf0bc9e0.bw AciCC3_H3K4me3_chip_signal_382fc8cab2110bff6909a155562e5c2e.bw AciCC3_NR4A3_chip_signal_23e1c3d61c68d7b669dab45a545eb5c0.bw Parotid_Gland3_CTCF_chip_signal_0a669d678c6a83b7b1cda7ecd6e1fc5b.bw Parotid_Gland3_H3K27Ac_chip_signal_a19976272ce5927c945a480276abd635.bw Parotid_Gland3_H3K27me3_chip_signal_3090f6a3a4248ad3c80d93e5b3324c37.bw Parotid_Gland3_H3K4me3_chip_signal_5392b73b61eb11051b11d4e0a8267221.bw Parotid_Gland3_NR4A3_chip_signal_024f8f668cbf06ca7d33763ed17da42a.bw Methylation call files (bedGraph format) AciCC1_methylationCalls_8906b27a616fa5b11eee438f79858517.bedGraph.gz AciCC2_methylationCalls_b5e7cd8f983ccd7e8b3655707f49fe79.bedGraph.gz AciCC3_methylationCalls_7808c8dc9dc49587e228b0f6171fc91c.bedGraph.gz Parotid_Gland1_methylationCalls_966b8f202b698a9ef90d2c6d22621056.bedGraph.gz Parotid_Gland2_methylationCalls_cc1afa52f36543bbf82c2776cbffde08.bedGraph.gz Parotid_Gland3_methylationCalls_5d74b8d198561823791b010427261a22.bedGraph.gz RNA-Seq tables Mouse count_matrix_mouse.csv fpkm_matrix_mouse.csv RNA-Seq tables Human count_matrix_human.csv fpkm_matrix_human.csv, {"references":["Haller, F. et al. Enhancer hijacking activates oncogenic transcription factor NR4A3 in acinic cell carcinomas of the salivary glands. Nature Communications 10, 1-13 (2019)."]}

Supplemental material, sj-pdf-1-jbx-10.1177_2472555220984372 for A Tale of Two Tails: Efficient Profiling of Protein Degraders by Specific Functional and Target Engagement Readouts by Alexey L. Chernobrovkin, Cindy Cázares-Körner, Tomas Friman, Isabel Martin Caballero, Daniele Amadio and Daniel Martinez Molina in SLAS Discovery

List of primers and probes. This file contains all primer sequences used for cloning and mutagenesis of the luciferase reporter plasmids, as well as the UPL primers and probes used for qRT-PCR. (DOCX 17 kb)

Crosstalk between growth factors (GFs) and steroid hormones recurs in embryogenesis and is co-opted in pathology, but underlying mechanisms remain elusive. Our data from mammary cells imply that the crosstalk between the epidermal GF and glucocorticoids (GCs) involves transcription factors like p53 and NF-κB, along with reduced pausing and traveling of RNA polymerase II (RNAPII) at both promoters and bodies of GF-inducible genes. Essentially, GCs inhibit positive feedback loops activated by GFs and stimulate the reciprocal inhibitory loops. As expected, no alterations in DNA methylation accompany the transcriptional events instigated by either stimulus, but forced demethylation of regulatory regions broadened the repertoire of GF-inducible genes. We report that enhancers, like some promoters, are poised for activation by GFs and GCs. In addition, within the cooperative interface of the crosstalk, GFs enhance binding of the GC receptor to DNA and, in synergy with GCs, promote productive RNAPII elongation. Reciprocally, within the antagonistic interface GFs hyper-acetylate chromatin at unmethylated promoters and enhancers of genes involved in motility, but GCs hypoacetylate the corresponding regions. In conclusion, unmethylated genomic regions that encode feedback regulatory modules and differentially recruit RNAPII and acetylases/deacetylases underlie the crosstalk between GFs and a steroid hormone.

Growth factors promote tumor growth and metastasis. We found that epidermal growth factor (EGF) induced a set of 22 microRNAs (miRNAs) before promoting the migration of mammary cells. These miRNAs were more abundant in human breast tumors relative to the surrounding tissue, and their abundance varied among breast cancer subtypes. One of these miRNAs, miR-15b, targeted the 3' untranslated region of MTSS1 (metastasis suppressor protein 1). Although xenografts in which MTSS1 was knocked down grew more slowly in mice initially, longer-term growth was unaffected. Knocking down MTSS1 increased migration and Matrigel invasion of nontransformed mammary epithelial cells. Overexpressing MTSS1 in an invasive cell line decreased cell migration and invasiveness, decreased the formation of invadopodia and actin stress fibers, and increased the formation of cellular junctions. In tissues from breast cancer patients with the aggressive basal subtype, an inverse correlation occurred with the high expression of miRNA-15b and the low expression of MTSS1. Furthermore, low abundance of MTSS1 correlated with poor patient prognosis. Thus, growth factor-inducible miRNAs mediate mechanisms underlying the progression of cancer.